Describing the competencies required by a profession is essential for aligning online profiles of job seekers and job advertisements. Comparing the competencies described within each context has typically not be done, which has generated a complete disconnect in language between them. This work presents an approach for the alignment of online profiles and job advertisements, according to knowledge and skills, using measures of lexical, syntactic and taxonomic similarity. In addition, we use a ranking that allows the alignment of the profiles to the topics of a thesaurus that define competencies. The results are promising, because the combination of the measures of similarity with the alignment with thesauri of competencies offers robustness to the process of generation of professional competence descriptions. This combination allows dealing with the common problems of synonymy, homonymy, hypernymy/hyponymy and meronymy of the terms in Spanish. This research uses natural language processing to offer a novel approach for assessing the match of the competencies described by the applicants and by the employers, even if they use different terminology. The resulting approach, while developed in Spanish for computer science jobs, can be extended to other languages and domains, such is the case of recruitment, where it will contribute to the creation of better tools that give feedback to job seekers about how to best align their competencies with job opportunities.

This work addresses the problem of student dropout in the university environment, where students abandon their studies at any phase of their career, for reasons ranging from personal and economic factors to academic dissatisfaction. This results in the student not starting the degree, or dropping out in early cycles and some cases in the final cycles, affecting the continuity of the study program, and at the same time the students do not reach their profession. With the help of Artificial Intelligence, it is possible to find patterns that allow us to avoid these scenarios early, providing universities with useful information to establish mitigation strategies. This research proposes a prediction model for student dropout in critical subjects, using classic Machine Learning techniques and advanced Deep Learning algorithms, integrated under the KDD method, for analysis of the academic information of the Computer Science degree over 2 years. The results of the model allow us to identify the techniques and algorithms with greater precision and at the same time recognize patterns that establish the dropout trend in core and pre-professional subjects.